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Assessment of machine and deep learning Approaches for Fault Diagnosis in Photovoltaic Systems Using Infrared Thermography

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REMOTE SENSING 2023年第6期15卷 1686-1686页

作者： Boubaker, Sahbi Kamel, Souad Ghazouani, Nejib Mellit, Adel Univ Jeddah Coll Comp Sci & Engn Dept Comp & Network Engn Jeddah 21959 Saudi Arabia Northern Border Univ Coll Engn Dept Civil Engn Ar Ar 73222 Saudi Arabia Abdus Salam Int Ctr Theoret Phys ICTP Str Costiera I-34151 Trieste Italy

Nowadays, millions of photovoltaic (PV) plants are installed around the world. Given the widespread use of PV supply systems and in order to keep these PV plants safe and to avoid power losses, they should be carefully protected, and eventual faults should be detected, classified and isolated. In this paper, different machine learning (ML) and deep learning (DL) techniques were assessed for fault detection and diagnosis of PV modules. First, a dataset of infrared thermography images of normal and failure PV modules was collected. Second, two sub-datasets were built from the original one: The first sub-dataset contained normal and faulty IRT images, while the second one comprised only faulty IRT images. The first sub-dataset was used to develop fault detection models referred to as binary classification, for which an image was classified as representing a faulty PV panel or a normal one. The second one was used to design fault diagnosis models, referred to as multi-classification, where four classes (Fault1, Fault2, Fault3 and Fault4) were examined. The investigated faults were, respectively, failure bypass diode, shading effect, short-circuited PV module and soil accumulated on the PV module. To evaluate the efficiency of the investigated models, convolution matrix including precision, recall, F1-score and accuracy were used. The results showed that the methods based on deep learning exhibited better accuracy for both binary and multiclass classification while solving the fault detection and diagnosis problem in PV modules/arrays. In fact, deep learning techniques were found to be efficient for the detection and classification of different kinds of defects with good accuracy (98.71%). Through a comparative study, it was confirmed that the DL-based approaches have outperformed those based on ML-based algorithms.

关键词： photovoltaic modules fault detection and diagnosis machine learning and deep learning hybrid model transfer learning

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Digital Watermarking Using Artificial Intelligence: Concept, Techniques, and Future Trends

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SECURITY AND PRIVACY 2025年第1期8卷

作者： Dandooh, Azza El-Fishawy, Adel S. Hemdan, Ezz El-Din Egyptian Acad Engn & Adv Technol EAE&AT Dept Elect Engn Cairo Egypt Menoufia Univ Fac Elect Engn Dept Elect & Commun Menoufia Egypt Menoufia Univ Fac Elect Engn Dept Comp Sci & Engn Menoufia Egypt Prince Sultan Univ Struct & Mat Res Lab Riyadh Saudi Arabia

One of the most widely used tools for information security is digital watermarking. Multimedia content, including text, audio, video, and images, frequently uses it. Utilizing watermarking requires different goals. These goals encompass copyright authentication as well as protection. It might be necessary for the watermark to be strong or fragile. Applications requiring copyright authentication must include a fragile watermark. However, copyright protection necessitates a strong watermark. For resolving a variety of intelligence-related issues, machine learning (ML) and deep learning (DL) are solid options. Although it works well for watermarking, it is less effective at more common tasks like regression, classification, and prediction. A thorough analysis of watermarking with popular technologies like artificial intelligence (AI), DL, and ML is presented in this article. A summary of watermarking, background information, and the most interesting applications are also covered.

关键词： artificial intelligence digital watermarking machine learning and deep learning

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Predictive modeling for organ transplant rejection: The promising role of artificial intelligence and machine learning

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Transplant Immunology 2025年 90卷 102223-102223页

作者： Suvvari, Tarun Kumar Department of Medicine Rangaraya Medical College Andhra Pradesh Kakinada India Squad Medicine and Research (SMR) Andhra Pradesh India

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Estimation of mangrove heights and aboveground biomass using UAV-LiDAR, Sentinel-1 and ZY-3 stereo images

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ECOLOGICAL INFORMATICS 2025年 88卷

作者： Fu, Bolin Wei, Yingying Jiang, Linhang Yao, Hang Li, Xiaomin Yang, Yanli Jia, Mingming Sun, Weiwei Guilin Univ Technol Coll Geomat & Geoinformat Guilin 541006 Peoples R China Hainan Normal Univ Coll Geog & Environm Sci Haikou 571158 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Key Lab Wetland Ecol & Environm Changchun 130102 Peoples R China Ningbo Univ Dept Geog & Spatial Informat Tech Ningbo 315211 Peoples R China

Mangroves are crucial blue carbon ecosystems that are essential for promoting sustainable global development. Tree height is a key indicator of mangrove health;however, accurately estimating mangrove height in complex coastal environments is challenging. In this study, we constructed mangrove height inversion models using multiple types of remote sensing data and machine learning algorithms (partial least squares regression (PLSR), random forest (RF), and mixture density network (MDN)). We evaluated the performance of UAV-LiDAR point clouds, ZY-3 stereo images, and Sentinel-1 polarimetric and interferometric data in mangrove height inversion, and explored the accuracy differences among the dominant species. We also estimated the aboveground biomass of different dominant mangrove species to better understand their ecological functions and health conditions. The results showed the following: (1) The canopy height model and height variables of the LiDAR point clouds, DVI and near-infrared bands of the ZY-3 stereo images, and polarimetric decomposition parameters of the Sentinel-1 SAR images were more sensitive to mangrove heights. (2) The LiDAR point clouds and Sentinel-1 SAR images achieved the highest inversion accuracy when using the RF algorithm, with R2 values of 0.875 and 0.685, respectively. The ZY-3 stereo images based on MDN obtained the optimal inversion results (R2 = 0.719), with an improvement ranging from 0.143 to 0.198 when compared to the PLSR and RF algorithms. (3) Avicennia marina was associated with the highest estimation accuracy (R2 = 0.897) compared to the other dominant mangrove species. Aegiceras corniculatum and Avicennia marina were associated with the highest inversion accuracy within the height range of 2-3 m (R2 = 0.925, R2 = 0.814, respectively), whereas Kandelia candel yielded the optimal inversion results at the height range of 1-2 m (R2 = 0.652). (4) The aboveground biomass of Aegiceras cornicatum and Kandelia candel ranged from 20.176 t

关键词： Mangroves Height inversion Aboveground biomass UAV-LiDAR point cloud Stereo images Polarimetric interferometric images machine learning and deep learning

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EARLY-SEASON CROP CLASSIFICATION UTILIZING TIME SERIES BASED deep learning WITH MULTI-SENSOR REMOTE SENSING DATA

EARLY-SEASON CROP CLASSIFICATION UTILIZING TIME SERIES BASED...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Fei, Chuhong Li, Yifeng McNairn, Heather Lampropoulos, George AUG Signals Ltd 73 Richmond St W Toronto ON M5H 4E8 Canada Agr & Agri Food Canada 960 Carling Ave Ottawa ON K1A 0C6 Canada

ISBN: (纸本)9798350360332;9798350360325

Early-season crop type classification provides crucial information for monitoring in-season crop growth and predicting crop diseases, supporting global food security. In remote sensing practices for agriculture, it is technically challenging to perform early-season prediction mainly due to factors such as significant soil interference to crops on the ground and limited availability of remote sensing data. In this work, we propose a time series based machine/deep learning approach which is able to utilize all useful features of satellite images captured by multiple sensors (RCM, Sentinel-1, and Sentinel-2) in the early season. The algorithm is implemented iteratively on data sequences such that when a new satellite image becomes available, new features will be appended to existing time series data, thus providing a more accurate prediction map in updated regions. In the study, four (4) machine/deep learning algorithms, including XGBoost, CNN, and LSTM, are evaluated for their performance in terms of prediction accuracy and computational complexity. The proposed approach is tested in three study areas in the Canadian Prairie provinces in the 2021 season. Crop type mapping results are obtained from the available early-season images from June 10 to June 30, 2021, with extended results to July 31, 2021. The validation results on independent crop survey data show that the proposed algorithms can achieve crop type classification accuracy around 85% at the end of June and above 90% at the end of July.

关键词： Early-season crop classification Timeseries analysis machine learning and deep learning Remote sensing

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deep learning and TextBlob Based Sentiment Analysis for Coronavirus (COVID-19) Using Twitter Data

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2022年第1期31卷

作者： Chandrasekaran, Ganesh Hemanth, Jude Karunya Inst Technol & Sci Dept ECE Coimbatore Tamil Nadu India

The novel Coronavirus (COVID-19) has affected the normal life of people around the world and has forced them to maintain social distance, self-quarantine and closing of many businesses. So, the people wish to share the information regarding the pandemic through social media e.g. Twitter. The key objective of this paper is to identify the sentiment associated with the pandemic by utilizing the tweets regarding the Coronavirus (COVID-19) and by using the python libraries to perform the task. We analyze the sentiment of people when Coronavirus has attained a peak level using the machine, deep learning techniques and TextBlob. We have classified the sentiments into positive and negative classes using the machine learning (NB, SVM, Logistic regression) approaches and deep learning-based Bi-LSTM model. The Bi-LSTM approach has achieved better accuracy (0.87) compared to the traditional machine learning models for Twitter sentiment classification. From the overall analysis, we could conclude that people are confident and they express more positivity towards the recovery from the COVID-19 pandemic. Such an analysis will help the policy and decision-makers to address the needs of the public appropriately.

关键词： Coronavirus (COVID-19) Twitter sentiment analysis machine learning and deep learning

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The use of deep learning algorithms to predict mechanical strain from linear acceleration and angular rates of motion recorded from a horse hoof during exercise

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2022年 216卷 106972-106972页

作者： Mouloodi, Saeed Rahmanpanah, Hadi Martin, Colin Gohery, Scott Burvill, Colin Davies, Helen M. S. Univ Melbourne Dept Mech Engn Melbourne Vic Australia Sperero Pty Ltd Sydney NSW Australia Univ Melbourne Dept Vet Biosci Melbourne Vic Australia

Mobility solutions offered by living creatures have inspired engineers to capture their locomotion patterns and then develop novel animal-like robots that use legs for locomotion. Exploring relationships amongst mechanical responses and kinematic parameters is essential for both inventing these robots and enhancing computational techniques. Establishment of accurate physical models to quantify mechanical responses of biological systems is challenging because the corresponding variables are multidimensional, dynamic and highly nonlinear. This encourages the advent of data-driven models in mechanical sciences. This paper delves into the use of feed forward and time-series (dynamic) artificial neural networks (ANN) to analyse experimental data recorded from a racing horse exercised up to 60 km/h to then relate hoof mechanical strain to kinematic parameters recorded experimentally. An inertial measurement unit that was comprised of a sensor and data acquisition system package was designed and mounted on the horse's hoof to measure linear accelerations and angular rates of motion. In addition, an instrumented Aluminium horseshoe that was designed and manufactured and contained: (1) inertial sensors including three orthogonal accelerometers and three orthogonal rate gyroscopes;and, (2) a strain gauge located at the middle of the shoe. The horse was warmed up in a steady gallop at around 35 km/h for 1 km then turned around and galloped at increasing speed to 68 km/h back to the finishing line. Nine kinematic parameters, measured during horse exercise, formed the ANNs input variables: hoof linear accelerations along three orthogonal directions (axe,a(y),a(z)), hoof angular rates of motion along three orthogonal directions (G(x), G(y), G(z)), shoe linear accelerations along three orthogonal directions (a(x)(s),a(y)(s),a(z)(s)), and time. Feedforward and time series ANNs trained using 1,000,000 experimental instances offered excellent reliability for the prediction of mechani

关键词： Artificial neural network (ANN) machine learning and deep learning Biomechanics Complex biological system Kinematics and mechanical strain Data-driven modelling framework

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SorghumWeedDataset_Classification and SorghumWeedDataset_Segmentation datasets for classification, detection, and segmentation in deep learning

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DATA IN BRIEF 2024年 52卷 109935页

作者： Justina, Michael J. Thenmozhi, M. SRM Inst Sci & Technol Sch Comp Dept Comp Sci & Engn Kattankulathur Campus Chennai 603203 India SRM Inst Sci & Technol Sch Comp Dept Networking & Commun Kattankulathur Campus Chennai 603203 India

The intuition behind this data acquisition is to encourage research for addressing the problem of weeds in agriculture through computer vision applications. Data is acquired in the form of images from uniform and random crop-spacing fields. In other words, we have taken a step forward to identify weeds from fields that follow any method of sowing, which ultimately leads to the transformation of traditional agriculture into precision agriculture. Sorghum crop and its associated weeds are chosen as the research objects during this process. These acquired data are used in framing two datasets. The first dataset termed 'SorghumWeedDataset_Classification' is a crop-weed classification dataset created with 4312 data samples for addressing crop-weed classification problems. The second dataset termed 'SorghumWeedDataset_Segmentation' is a crop-weed segmentation dataset that contains 5555 manually pixel-wise annotated data segments from 252 data samples for addressing crop-weed localization, detection, and segmentation problems. All data samples are acquired in April and May 2023 from Sri Ramaswamy Memorial (SRM) Care Farm, Chengalpattu district, Tamil Nadu, India. Manually annotated data samples and data segments are verified by agronomists. The datasets are made publicly available to the research community to solve the crop-weed problems using state-of-the-art image processing, machine learning, and deep learning algorithms. To the best of our knowledge, these are the first open-access crop-weed research datasets from Indian fields for classification and segmentation to deal with weed issues in uniform and random crop-spacing fields. However, other available datasets (from Indian fields) are either non-research datasets or available on subscription/request. (c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： Uniform and random crop-spacing Crop-weed identification Crop-weed dataset Sorghum_dataset Research_data Precision agriculture Autonomous weeding machine learning and deep learning

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A deep Transfer learning Approach to Enhance Network Intrusion Detection Capabilities for Cyber Security

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第4期13卷 843-855页

作者： Das, Abhijit Pramod VTU Belagavi CSE PESITM Shivamogga India BNMIT Dept CSE Bangalore Karnataka India VTU Dept ISE PES Inst Technol & Management Shivamogga India

Cyberattacks are on the rise, making technology companies increasingly prone to data theft. Recent research has focused on constructing cognitive models for traffic anomaly detection in a communication network. Many of these exper-iments resulted in data packets recorded by technologies like Wireshark. These datasets provide high-dimensional data relating to benign and malicious data packets. Recent research has mostly focused on developing machine learning, and deep learning systems o detect attack data packets in a network. Also, machine learning algorithms are currently trained to detect only known threats. However, with the growth of new cyberattacks and zero-day attacks, current algorithms are unable to detect unknown attacks. This research focuses on detecting rare attacks using transfer learning from a dataset of known attacks. deep learning outperforms explicit statistical modelling approaches by at least 21% for the dataset used. A preliminary survey of candidate deep learning architectures has been performed before testing for transferability and proposes a Convolutional Neural Network architecture that is 99.65% accurate in classifying attack data packets. The suggested CNN architecture trained with a known attack and then tested its performance on unknown attacks to assess transferability. For this model to extract sufficient information for transferability, the training samples must have more information. Only 20% of the dataset represents current threat data. Several strategies, such as innovative synthetic dataset-based training and bootstrapped dataset training, have been developed to overcome small training sets. A subset of training attacks is determined to optimise learning potential. This study finds training-testing attack pairings with good learning transferability. The most robust and stable relationships are found in DoS attack training-testing pairings. This study also presents model generalisation hypotheses. The dataset features and attack

关键词： Transfer learning convolutional neural network intrusion detection system cyber security machine learning and deep learning

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Simple to Complex, Single to Concurrent Sensor-Based Human Activity Recognition: Perception and Open Challenges

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IEEE ACCESS 2024年 12卷 93450-93486页

作者： Ankalaki, Shilpa Manipal Acad Higher Educ Manipal Inst Technol Bengaluru Dept Comp Sci & Engn Manipal 576104 Karnataka India

Human activity recognition (HAR) has attracted considerable research attention due to its essential role in various domains, ranging from healthcare to security, safety, and entertainment. HAR has undergone a paradigm shift from simple single-task detection to the more complex task of identifying multiple simultaneous activities as technology advances. A wide range of methods, including sensing modalities, identification algorithms, a specified list of recognized activities, and end application goals, have been used in the literature to investigate activities carried out by single individuals. However, there appears to be a research gap when it comes to scenarios in which several people engage in individual or concurrent activities. Although numerous reviews and surveys have previously addressed HAR, with the continual expansion of literature, there is a necessity for an updated assessment of the status of HAR literature. The system encompasses various operational modules, including data acquisition, noise elimination, and distortion reduction through preprocessing, followed by feature extraction, feature selection, and classification. Recent advancements have introduced state-of-the-art techniques for feature extraction and selection, which are categorized using traditional machine learning classifiers. However, a notable limitation is observed, as many of these techniques rely on basic feature extraction processes, hindering their capability to recognize complex activities. This article reviews 190 articles with respect to data collection, segmentation, feature extraction, energy efficiency, personalized models, and machine learning (ML) and deep learning (DL) approaches for sensor-based HAR. Open challenges and future enhancements of HAR are also discussed in this article.

关键词： Human activity recognition Task analysis Privacy Feature extraction Real-time systems Reviews Object recognition Sequential analysis Concurrent computing machine learning deep learning sequential activities concurrent activities interleaved activities machine learning and deep learning

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